System and method for improving the quality of thermal images

ABSTRACT

An image improvement system and method are disclosed that compensates for effects of optical scattering and pixel cross-talk on image quality in an imager employing a focal plane array. The method characterizes these effects on a test image, presents them as a set of stored numerical coefficients, and applies the coefficients during image processing.

TECHNICAL FIELD

The present disclosure relates to calibrating a thermal imager that usesa focal plane array to compensate for effects of optical scattering andpixel cross-talk on image quality.

BACKGROUND

The measured optical resolution of infrared radiometry images isgenerally known to be degraded as compared to theoretical values. Thedegradation can be caused by light scattering in the optical system.Typically, an imager uses an array of light sensing pixels called afocal plane array that is positioned at or near the focal plane of theoptical system of the imager in order to image a subject. However, pixelcross-talk which results in unwanted coupling of energy between pixelscan occur that has optical, thermal, and/or electrical origins, thuscontributing to the degradation of the measured image.

Digital image processing uses algorithms to perform image processing onpixel values of digital images. One type of image processing algorithmperforms spatial filtering where the algorithm is intended to identifypixels in a digital image at which the image brightness has gradients.In one technique, a spatial filter is applied to an image in order toenhance contrast at the edges of objects within the image, while stillpreserving the important structural properties of an image. However,application of edge enhancement algorithms alone to radiometry imageswould distort radiometric properties of the acquired thermal imageswithout further information about the source of the image degradation.

SUMMARY

An image improvement system and method are disclosed that compensatesfor the effects of optical scattering and pixel cross-talk onradiometric or photometric image quality in an imager employing a focalplane array. These effects are characterized on a test image and thenpresented as an individual set of numerical coefficients to be stored inthe instrument for use as a correction filter for image processing. Eachinstrument ideally has its own customized correction filter. By applyingthe individually pre-determined correction filter, object edge contrastis enhanced by a measured amount, such that the image radiometricallyimproves around the edges. As a result, the distance to spot ratio ofthe imaging instrument is significantly improved, especially whencharacterized at higher energy levels which is necessary for higheraccuracy applications.

BRIEF DESCRIPTION OF THE DRAWINGS

Examples of a radiometric image improvement system and method areillustrated in the figures. The examples and figures are illustrativerather than limiting.

FIG. 1A depicts a flow diagram illustrating an exemplary filtergeneration process, according to an embodiment of the disclosure.

FIG. 1B depicts a flow diagram illustrating an exemplary iterative testcalibration process, according to an embodiment of the disclosure.

FIG. 2 is a graphical representation of an example correction filterthat has been determined to represent the influence of neighboringpixels on a central pixel for a particular imaging instrument.

FIG. 3 shows an example of an image taken of a test target and anormalized signal across an edge in the rendered image.

FIG. 4 shows an example of the 1/D² scattering dependence arising fromscattering and reflections within a focal plane array.

FIG. 5 depicts a flow diagram illustrating an exemplary image correctionprocess for applying a matrix of correction coefficients as a spatialcorrection filter, according to an embodiment of the disclosure.

FIG. 6 shows an example of an unprocessed image of a D:S (distance:spot)resolution test plate.

FIG. 7A shows a block diagram of an example test target system used tocalibrate an imaging instrument.

FIG. 7B shows a block diagram of a radiometric improvement system thatcharacterizes the effects of optical scattering and/or pixel crosstalkfor use during image processing, according to an embodiment of thedisclosure.

DETAILED DESCRIPTION

Described in detail below is a method and apparatus for improving theimage quality of an imaging instrument that uses a focal plane array.The distribution of parasitic energy within a defined neighborhoodaround a pixel is determined in order to generate a spatial correctionfilter made up of a matrix of coefficients. This filter is then appliedto each pixel in the image to improve the accuracy of the energyreadings, where the calculated parasitic energy from each pixel signalis subtracted, as expressed in radiance units, prior to conversion totemperature values.

The following description and drawings are illustrative and are not tobe construed as limiting. Numerous specific details are described toprovide a thorough understanding of the disclosure. However, in certaininstances, well-known or conventional details are not described in orderto avoid obscuring the description.

Without intent to further limit the scope of the disclosure, examples ofinstruments, apparatus, methods and their related results according tothe embodiments of the present disclosure are given below. Reference inthis specification to “one embodiment” or “an embodiment” means that aparticular feature, structure, or characteristic described in connectionwith the embodiment is included in at least one embodiment of thedisclosure. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment, nor are separate or alternative embodiments mutuallyexclusive of other embodiments. Moreover, various features are describedwhich may be exhibited by some embodiments and not by others. Similarly,various requirements are described which may be requirements for someembodiments but not other embodiments.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Certain terms that are used todescribe the disclosure are discussed below, or elsewhere in thespecification, to provide additional guidance to the practitionerregarding the description of the disclosure. The use of examplesanywhere in this specification including examples of any terms discussedherein is illustrative only, and is not intended to further limit thescope and meaning of the disclosure or of any exemplified term.Likewise, the disclosure is not limited to various embodiments given inthis specification.

The terminology used in the description presented below is intended tobe interpreted in its broadest reasonable manner, even though it isbeing used in conjunction with a detailed description of certainspecific examples of the invention. Certain terms may even be emphasizedbelow; however, any terminology intended to be interpreted in anyrestricted manner will be overtly and specifically defined as such inthis Detailed Description section.

Generally, radiometric and photometric imagers tend to produce imagesthat have imperfections arising from the optics and/or electronics ofthe imager. For example, when there is a step transition between a firstlevel of illumination and a second level of illumination in a target tobe imaged, the resulting image can have some blurring due to opticalscattering and also time-domain signal transfer caused by latency infront-end electronics that is dependent upon the sampling rate. Thetechniques for correction described below can be applied to any imaginginstrument operating at any imaging wavelength.

Typically, the optical system of a thermal imaging instrument collectsinfrared energy from a round measurement spot and focuses it on adetector. Optical resolution is the ratio of the distance from theinstrument to the object to be measured, compared to the size of thespot being measured (D:S ratio). For example, a D:S of 20:1 means thatat a distance of 20 cm, the spot size of the instrument is 1 cm. Thus,for a given distance between an imaging instrument and the target to beimaged, the higher the optical resolution of the instrument, the smallerthe spot size, and the spot size must be smaller than the object to bemeasured in order to perform an accurate measurement.

The common working wavelength range for radiometric imagers is betweeneight and fourteen microns because these wavelengths fall within anatmospheric transmission window. However, any appropriate measurementwavelength may be used. Traditionally, thermal imaging instruments thatcan measure 90% of the energy have a 10% error when expressed in energyunits, and this corresponds to approximately a 6% error when expressedin temperature units. If a thermal imaging instrument is needed that hasa better temperature accuracy, it is necessary to measure acorrespondingly greater percentage of the energy with the imaginginstrument. For example, a measurement of 1% accuracy in the temperaturereading would require the imaging instrument to measure 98.4% of theactual energy. Generally, a measurement of 100% of the energy is definedfor a particular calibration geometry where the instrument is supposedto be accurate, but if the instrument is moved farther away from atarget of a given size (large D:S), less energy is collected, and thetemperature reading will become more inaccurate.

The negative effects of optical scattering and pixel cross-talk degraderadiometric or photometric image quality. Typically, the effects ofoptical scattering are seen with sharp edge transitions at the highestlevels of measured energy. Thus, the correction of images produced by animaging instrument involves the use of a target image that has at leastone sharp transition edge between uniformly illuminated areas havingdifferent illuminations. The goal of the analysis is to determine thedistribution of parasitic energy within a neighborhood around eachpixel.

FIG. 1A depicts a flow diagram illustrating an exemplary filtergeneration process 100A for generating a spatial correction filter forthe images generated by an imaging instrument. The filter generationprocess 100A is generally performed prior to the initial use of theimager by an instrument filter designer. However, the filter generationprocess 100A may be performed at any time during the life of the imagerinstrument. Typically, the process 100A occurs at the instrument factoryprior to delivery to an end user. Once the correction coefficients forthe filter have been determined by the process 100A, these parameterscan be stored and applied to each subsequent image taken by the imager.

At block 101, the images produced by the imager are evaluated by thefilter designer to determine whether the imager produces images ofuniform quality and with fairly uniform scattering properties. If theimage quality and the scatter distribution are sufficiently uniformacross the entire image, a single filter can be generated and thenapplied to process all images from that imager to improve theradiometric quality, where the coefficients of the filter are obtainedby using a single scattering function. However, if the properties of theimages produced by the imager change from one section of an image toanother section, then the applied filter should also changecorrespondingly in order to accurately process the different sections ofthe image. An imager that uses a wide angle lens would be onenon-limiting example where an image would be generated with sectionshaving different properties. The central portion of the image would havedifferent properties from the peripheral portion of the image. For eachregion of the image to which a particular filter is applied, however, itis assumed that within that region, the image quality and scattering arefairly uniform.

FIG. 2 is a graphical representation of an example filter 200 comprisinga matrix of coefficients that have been determined to represent theinfluence of neighboring pixels on a central pixel for a particularimaging instrument. The central pixel is located at coordinates (0,0).

A single correction filter or a set of correction filters to be appliedto specific sections of generated images can be developed and used for afamily of imagers that are designed using the same optical system. Atblock 105 during the filter design stage, the designer selects anoptimum filter shape and size for the filter generation process 100A. Ingeneral, a circular filter shape would match the shape of the opticsused in the imager. However, a square or rectangular shape is generallypreferred for reducing processing time. In one embodiment, as shown inFIG. 2, the filter shape can be selected to be a square filter havingthe same number of pixels on a side; the filter shown in FIG. 2 is 11pixels long by 11 pixels wide and can be specified in a matrix formatfor matrix calculations. However, the filter shape can be selected to beany convenient shape and to have any appropriate pixel dimensions.

Next, at block 110 the filter designer selects an appropriate testtarget that has at least one linear transition edge slightly skewed fromeither a vertical position or a horizontal position, where thetransition is between two approximately uniformly illuminated areashaving different illuminations. One non-limiting example of anappropriate target is shown at the top of FIG. 3. The target has twouniformly illuminated areas that are white and gray in FIG. 3, and thereis a sharp linear transition edge between the two areas. In oneembodiment, the illumination source of the target is round, and aportion of the source is covered with a piece of metal, such asaluminum, where the metal provides a darker illumination area ascompared to the source. The transition edge between the two areas is ata small angle to the vertical or horizontal, not exactly vertical orhorizontal. If the transition edge were perfectly vertical orhorizontal, there would only be one transition pixel between the twoareas, so it would be difficult to determine how bright the pixel is atthe transition edge. However, if the transition edge were skewed at asmall angle to the vertical or horizontal, pixel lines (horizontal orvertical, respectively) crossing the transition curve edge would havedifferent geometrical phases relative to a neighboring pixel line.

Although the transition edge should be at a small angle, the angle atwhich the transition edge is with respect to a vertical or horizontalline is not critical, as long as the transition edge crosses at leastone pixel from the top to the bottom of the analysis area if the angleis close to vertical, or from the left to the right of the analysis areaif the angle is close to horizontal. An example analysis area is shownby the rectangle in the top picture in FIG. 3. With a transition lineclose to vertical, the pixels in horizontal rows would be analyzed bythe filter designer for optical scattering in the horizontal direction,and with a transition line close to horizontal, the pixels in verticalcolumns would be analyzed for optical scattering in the verticaldirection. If all of the scattering effects to be characterized areisotropic, then only pixels in one direction, either vertical orhorizontal, need to be analyzed.

In one embodiment, the scattering in both the horizontal and verticaldirections would be characterized to determine if there are anynon-isotropic scattering effects.

Next, at block 115, a test image of the test target is taken by thefilter designer with the instrument to be calibrated. Because the testimage is static, multiple images, even up to 100 or more images, of thetarget can be taken and then averaged at block 117 in order to reducenoise.

At block 120, the filter designer uses a processor to perform aniterative test calibration to determine the distribution of parasiticenergy within an analysis area by determining the characteristicequations for scattering and other optical and/or electronic effects andthen curve fitting the test data to the equations.

A few assumptions are made during the analysis and determination of thedistribution of parasitic energy at block 120. The first assumption isthat in order to correct an image, each pixel in a captured imagereceives parasitic energy from its neighboring pixels, and the parasiticenergy is proportional to a difference in illumination between the firstpixel and the neighboring pixel with the parasitic energy. This impliesthat the response of the imaging system to brightness levels and changesin brightness levels are relative, not absolute.

Alternatively, a non-linear distribution of brightness could beaccounted for by applying a correction filter as a function of thepercent change in brightness between pixels. This correction would beapplied to the energy levels of a captured image prior to conversion totemperature values because the conversion of the received energy by theimaging instrument to temperature is also non-linear.

The next assumption is that the parasitic energy distribution arisesprimarily from isotropic scattering, where the parasitic energy istransferred equally in all directions and is reduced as a function ofdistance from the influencing pixel to the pixel receiving the parasiticenergy. FIG. 4 shows an example of a 1/D² scattering dependence, where Dis the distance from the center pixel 422 to one of the neighboringpixels, for example pixel 424. The isotropic scattering is caused byreflections from the focal plane array window on each of the pixels, forexample window 425 on pixel 420. Thus, the light 410 entering the focalplane array of the imaging instrument would all be detected by pixel 422if there were no scattering caused by non-specular reflections. However,as an example, scattering of light 410 occurs off of the window of pixel422. Because the actual scattering is difficult to measure, empiricaldata is used to determine the scattering dependency, and the scatteringmechanism was found to closely follow a 1/D² scattering dependence, asillustrated in FIG. 4. Reflection 434 reflects off the inside coveringwindow 440 of the focal plane array and is captured by pixel 423,reflection 432 reflects off window 440 and is captured by pixel 424, andreflection 430 is reflection off window 440 and is captured by pixel425. The closer a neighboring pixel is to the center pixel, where thelight first impinges, the more energy that neighboring pixel willreceive. One reason that the 1/D² scattering dependence characterizesthe scattering so well is that covering window 440 is so close to thepixels. Although the 1/D² scattering dependence has been found tocharacterize the scatter in the focal plane array of the imaginginstrument well, other scattering functions may also be used tocharacterize the scattering, for example higher-order inverse polynomialfunctions.

During the determination of the coefficients by the filter designer, theactual averaged image pixel data calculated at block 117 is curve fit tothe chosen scattering dependence. Curve fitting is performed over aselected analysis area. The sharp transition edge or edges shouldcompletely cross the analysis area. Also, the analysis area should besignificantly larger than the selected filter size. Further, theanalysis area should be representative of the filter application area,while test target quality should be substantially uniform across theanalysis area. One example of an analysis area is the faint rectangleshown in the top of FIG. 3. Note that the analysis area boundary in FIG.3 is not near the non-uniform round edge of the source, nor is it nearthe stray reflections on the left of this figure.

There may also be another form of parasitic energy distribution basedupon time-domain signal transfer during sampling of the image focalplane array by the, where the parasitic energy is a portion of thesignal change from the brightness level of a previous pixel. Thetime-domain signal transfer is caused by latency in front-endelectronics and depends upon the sampling rate. Thus, for a very slowsampling rate, the latency in the electronics would not be evident, andno parasitic energy distribution component would arise from this source.However, a typical sampling rate is sufficiently fast that a noticeablenon-isotropic contribution arises from the electronics that correspondsto the scanning direction. When pixels in an image are scanned by thefocal plane array, only the pixels that precede a particular pixel inorder and time of scanning will contribute to the brightness of thatparticular pixel when correcting an image. The line of pixels above andbelow that particular pixel will have very low to negligiblecontributions that are not accounted for here. Based upon empirical dataarising from the electronics in the imaging system, the filter designerselects an appropriate equation that describes the data, similar to theprocess for determining the scattering dependency described above. Then,the actual averaged image data calculated at block 117 is used to curvefit to a selected time domain signal transfer equation. In oneembodiment, the curve fitting to the data for the scattering dependencyand time domain signal transfer equations can be done simultaneously,rather than sequentially.

In order to capture non-isotropic parasitic energy distribution, atarget having two sharp transition edges, one close a vertical positionand one close to a horizontal position, can be used. Scattering in thehorizontal direction is determined by analyzing the pixels in horizontallines across a nearly vertical transition edge, and scattering in thevertical direction is determined by analyzing the pixels in verticallines across a nearly horizontal transition edge.

The details for performing the test calibration of block 120 in FIG. 1Aare shown in test calibration process 100B in FIG. 1B. At block 140, theprocessor generates a local difference matrix for a particular imagepixel, where the local difference matrix has the same dimensions as thefilter selected at block 105. Typically, the local difference matrixwill be centered around the particular image pixel being analyzed.Because the test target used to generate the image has only twoillumination levels, level 1 and level 2 which are both known, therewill only be two numerical values within the local difference matrix foreach image pixel, namely, (pixel brightness—level 1) and (pixelbrightness—level 2).

At block 145, the processor performs an element by element matrixmultiplication using the local difference matrix calculated at block 140and a correction filter. The determination of the coefficients for thecorrection filter is an iterative process. The first time a correctionfilter is used, a standard or arbitrary starting correction filter canbe used that is subsequently adjusted upon future iterations. Thecontribution from each of the neighboring pixels, as determined at block145, is then summed up at block 150. The sum of the contributions fromthe neighboring pixels is associated with the image pixel for futurematrix for operations.

At decision block 155, the processor decides if the blocks 140, 145, and150 have been executed for all pixels within the analysis area. Ifgeneration of the local difference matrix, element by element matrixmultiplication, and summation of contributions from neighboring pixelshave not been executed for all pixels (block 155—No), the processreturns to block 140 where a local difference matrix is generated foranother pixel in the analysis area. If the steps have been performed forall pixels in the analysis area (block 155—Yes), the process continuesto decision block 160.

At decision block 160, the processor decides if the sum of thecontributions from block 150 matches the local difference matrix fromblock 140. If the matrices match (block 160—Yes), the filtercoefficients are stored at block 165. The matrix of filter coefficientsthat were determined for the imaging instrument is stored in memory foruse in processing subsequent images taken by that imager. The matrix ofcoefficients can be stored in memory at the imaging instrument or inmemory at an external computer for post-processing of images. Becausethe matrix of coefficients is particular to the imager, the coefficientsshould be stored with the images for post-processing. The process endsat block 198.

If the matrices do not match (block 160—No), at block 165, thecoefficients in the filter matrix are adjusted by the processoraccording to the filter designer's instructions. Then the processreturns to block 140 where a local difference matrix is generated for apixel in the analysis area, and the iterative process continues. Theiterative process implements the curve fitting of the test data to thecharacteristic equations described above.

The iterative test calibration determines the appropriate numericalcoefficients to use to correct the sharpness of a transition edge in theimage so that it is as close as possible to the theoretical stepresponse. If the brightness of the image is uniform, then the localdifference matrix will be a null matrix, and the correction filter willhave no impact. Then the filter designer attempts to adjust thenumerical coefficients iteratively

Referring to filter 200 in FIG. 2, notice that except for the pixellocated at coordinates (−1,0), the filter is symmetrical around thecentral pixel, indicating that the scattering mechanism is isotropic.The asymmetrical contribution from the pixel at (−1,0) arises from thetime domain signal transfer of the electronics, as discussed above.Thus, the focal plane array was scanned from left to right, and thepixel at (−1,0) was the pixel scanned immediately prior to the centralpixel at (0,0).

Returning to the filter generation process 100A, at block 125 in FIG. 1Athe different correction zones, if any, of the generated images, thefilter dimensions (size and shape) for each filter for the individualcorrection zones, and the particular equations are stored. These storedparameters can be used to generate the numerical coefficients for theindividual filters at any time during the life of the imaginginstrument. However, typically the filters can be generated and storedat the factory in memory within the imaging instrument or within anexternal image processor.

At decision block 130, the designer decides whether there are any otherimage sections for the particular imager for which a filter needs to bedesigned. If there is another section (block 130—Yes), the processreturns to block 105 where the designer selects an appropriate filtershape. If the image sections each have a corresponding correction filteralready designed (block 130—No), the process ends at block 199.

FIG. 5 depicts a flow diagram illustrating an exemplary correctionprocess 500 for applying a matrix of correction coefficients as aspatial correction filter to an image, according to an embodiment of thedisclosure. The correction filter can be applied to an image either inreal time or during post-processing of the image. Further, thecorrection filter can be performed in the imaging instrument or on anexternal processor such as a computer.

At block 503, an image is captured using an imaging instrument that hasa focal plane array.

Then at block 505, a predetermined section of image is selected alongwith its corresponding correction filter. If the imager produces fairlyuniform images, the imager only has a single correction filter which isapplied to the entire image.

For the particular section of the image that is being processed, atblock 515 a processor subtracts the brightness of a first pixel from itsneighboring pixels in radiance units, before conversion to temperatureunits. The number of neighboring pixels for which this calculation isperformed depends upon the size of the filter that is being applied. Forthe example, in the filter shown in FIG. 2, the filter is square with 11pixels on a side. Thus, a square local difference matrix having 11pixels on a side is calculated at block 515. The center pixel atcoordinates (0,0) corresponds to the pixel itself, thus the brightnessdifference is zero here. The amount of correction to be applied for thefilter shown in FIG. 2 is shown on the z-axis.

Alternatively, as described above, a non-linear distribution ofbrightness could be accounted for by applying a correction as a functionof the percent change in brightness between a central pixel andneighboring pixels.

At block 520, an element by element matrix multiplication is performedusing the local difference matrix calculated at block 515 and thecorrection filter determined with the filter generation process 100A andthe test calibration process 100B. This technique corrects the sharpnessof a transition edge in the image so that it is as close as possible tothe theoretical step response. If the brightness of the image isuniform, then the local difference matrix will be a null matrix, and thecorrection filter will have no impact.

The contribution from each of the neighboring pixels, as determined atblock 520, is then summed up at block 525. At block 530, thesecontributions are added to the central pixel to obtain a correctedbrightness. Then at block 532, the contribution of each neighboringpixel to the center pixel is subtracted from the brightness of thatparticular neighboring pixel in order to obtain the corrected pixelbrightness.

The goal of the analysis of correction process 500 is to have therendering of a sharp transition edge approach an ideal step response.The bottom graph of FIG. 3 shows a transition edge prior to correctionwhich has a noticeably rounded response that is several pixels in widthnear the 95-100% energy signal range. In contrast, the transition afterthe correction filter has been applied has a sharper step response inthe same energy signal range. As can be seen in this graph, formeasurements at lower energy levels, for example 50%, the correctionfilter does not noticeably improve the image quality. Thus, thecorrection filter technique is most useful when measuring an image athigh energy and high accuracy levels.

At decision block 535, the system determines if there are any pixelswithin the selected section of the image whose brightness level has notbeen corrected using the correction filter. If there are more pixels tobe processed (block 535—No), the process returns to block 515. If allthe pixels in the section have been processed (block 535—Yes), theprocess continues to decision block 540.

At decision block 540, the system determines if there are any othersections in the image to be processed using a different correctionfilter. If there is another section (block 540—Yes), the process returnsto block 505 to select another image section and the correspondingcorrection filter. If there are no more unprocessed sections (block540—No), the process ends at block 599.

FIG. 6 shows an example of an unprocessed image 600 of a D:S(distance:spot) resolution test plate. The image 600 has been averagedover hundreds of images taken of the test plate in order to get betterresolution and decrease the noise. The images have been taken at a fixeddistance.

The resolution test plate is made up of holes of different sizes. Thelarger holes in the test plate have a lower D:S ratio, and the smallestholes have a D:S ratio that is beyond the instrument resolution. Therange of D:S ratios present in the resolution test plate allows thequantitative measurement of the improvement in the D:S ratio for animaging instrument after application of the correction process 500described above.

In order to quantitatively determine the improvement that the correctionfilter provides, a temperature measurement can be taken of theuncorrected image of FIG. 6 in a hole that corresponds to a particularD:S ratio, for example 50:1. Then the percent difference in the measuredtemperature value from the actual temperature of the source iscalculated. Next, the temperature is measured in smaller holes in thecorrected image that have a higher D:S ratio. By comparing the size ofthe hole in the corrected image that results in a similar percentdifference, an indication of the improvement in D:S ratio can beobtained.

In particular, for the hole 610 located at the cross-hairs in theuncorrected image of FIG. 6, the D:S ratio is 120:1, and the measuredtemperature was 316.7° C., while the actual background temperature was330° C., thus resulting in approximately a 4% error in the measuredtemperature which corresponds to approximately a 6% error in themeasured energy. In contrast, for the same hole in an image taken afterthe correction filter has been applied, the measured temperatureincreased to 327.2° C., resulting in approximately a 1% temperaturereading error, or 1.5% energy error, which is an improvement intemperature measurement accuracy on a small object, such as the hole610.

FIG. 7A shows a block diagram 700A of an example test target used tocorrect an imaging instrument. A test target can include a calibrationsource 710 and a mask 720.

The calibration source 710 should have a substantially uniformtemperature across its surface. One non-limiting example of acalibration source is a plate source produced by Hart Scientific, adivision of Fluke of Everett, Wash. The plate source can be round,square, or any other shape. The mask 720 is placed in front of thecalibration source 710 in order to provide one or more sharp transitionedges. Masks can be made from aluminum or any other suitable material.Further, the mask should be tilted at a small angle from vertical orhorizontal.

FIG. 7B shows a block diagram 700B of a radiometric improvement systemthat characterizes the effects of optical scattering and/or pixelcrosstalk for use during image processing, according to an embodiment ofthe disclosure. The radiometric improvement system 700B includes animager 775 to be characterized and a processing system 735 for applyingthe correction filter to images taken by the imager 775. The imager 775can include an optical system 770, electronics 780, and a focal planearray 790. The processing system 735 can include one or more processors730, one or more memory units 740, input/output devices 750, and powersupplies 760.

The optical system 770 includes any optical components used to image anobject onto a focal plane array 790. Optical components in the opticalsystem 770 can include, but are not limited to, lenses, mirrors, prisms,filters, and stops. The electronics 780 are used for sampling orscanning the focal plane array 790. The focal plane array 790 is anarray of light-sensing pixels used for imaging. A non-limiting exampleof a focal plane array includes a micro-bolometer array.

A processor 730 can be used to run image processing applications such asapplying a correction filter to one or more images. Memory units 740 caninclude, but are not limited to, RAM, ROM, and any combination ofvolatile and non-volatile memory. Memory units 740 can storecoefficients for one or more filters and any other parameters used tocalibrate or correct an imaging instrument such as gain and offsetcoefficients. Memory units 740 can also store image data. A power supply760 can include, but is not limited to, a battery. An input/outputdevice 750 can include, but is not limited to, triggers to start andstop the image processing system or to initiate other image processingfunctions, such as calibration or correction processes, visual displays,speakers, and communication devices that operate through wired orwireless communications.

Typically, processing system 735 is a part of the imager 775. However,the processing system 735 can be located external to the imager 775,thus the input/output device 750 can be used to communicate with one ormore imaging instruments. Further, an external processing system 735 canstore filter values for multiple imaging instruments, thus providing acentral calibration service.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the terms “connected,”“coupled,” or any variant thereof, means any connection or coupling,either direct or indirect, between two or more elements; the coupling ofconnection between the elements can be physical, logical, or acombination thereof. Additionally, the words “herein,” “above,” “below,”and words of similar import, when used in this patent application, shallrefer to this application as a whole and not to any particular portionsof this application. Where the context permits, words in the aboveDetailed Description using the singular or plural number may alsoinclude the plural or singular number respectively. The word “or,” inreference to a list of two or more items, covers all of the followinginterpretations of the word: any of the items in the list, all of theitems in the list, and any combination of the items in the list.

The above detailed description of embodiments of the disclosure is notintended to be exhaustive or to limit the teachings to the precise formdisclosed above. While specific embodiments of, and examples for, thedisclosure are described above for illustrative purposes, variousequivalent modifications are possible within the scope of thedisclosure, as those skilled in the relevant art will recognize. Forexample, while processes or blocks are presented in a given order,alternative embodiments may perform routines having steps, or employsystems having blocks, in a different order, and some processes orblocks may be deleted, moved, added, subdivided, combined, and/ormodified to provide alternative or sub-combinations. Each of theseprocesses or blocks may be implemented in a variety of different ways.Also, while processes or blocks are at times shown as being performed inseries, these processes or blocks may instead be performed in parallel,or may be performed at different times. Further any specific numbersnoted herein are only examples: alternative implementations may employdiffering values or ranges.

The teachings of the disclosure provided herein can be applied to othersystems, not necessarily the system described above. The elements andacts of the various embodiments described above can be combined toprovide further embodiments.

While the above description describes certain embodiments of thedisclosure, and describes the best mode contemplated, no matter howdetailed the above appears in text, the teachings can be practiced inmany ways. Details of the system may vary considerably in itsimplementation details, while still being encompassed by the subjectmatter disclosed herein. As noted above, particular terminology usedwhen describing certain features or aspects of the disclosure should notbe taken to imply that the terminology is being redefined herein to berestricted to any specific characteristics, features, or aspects of thedisclosure with which that terminology is associated. In general, theterms used in the following claims should not be construed to limit thedisclosure to the specific embodiments disclosed in the specification,unless the above Detailed Description section explicitly defines suchterms. Accordingly, the actual scope of the disclosure encompasses notonly the disclosed embodiments, but also all equivalent ways ofpracticing or implementing the disclosure under the claims.

1. A thermal imager, comprising: an optical system for thermal imaging;a focal plane array for capturing thermal images imaged by the opticalsystem, wherein the thermal images each comprise a plurality of valuesfor each pixel in the focal plane array; and a processor for applying afirst spatial filter to the plurality of values to enhance object edgecontrast in the thermal images.
 2. The thermal imager of claim 1 whereinthe first spatial filter comprises a plurality of first numericalcoefficients that represent a first distribution of pixel cross-talkenergy in a first section of the thermal images, the first spatialfilter is applied to enhance object edge contrast in the first sectionof the thermal images, and the first numerical coefficients aredetermined by: capturing a test image of a target with the thermalimager, wherein the target has at least one skewed transition edgebetween two substantially uniformly illuminated areas, and the two areashave different illumination levels, and further wherein the test imagecomprises a plurality of pixel test values; curve-fitting a first subsetof the pixel test values in the first section of the test image to afirst equation, wherein the first equation characterizes the firstdistribution of pixel cross-talk energy in the first section of the testimage; and using the first equation to generate the first numericalcoefficients as a function of their representative pixel positionswithin the first spatial filter.
 3. The thermal imager of claim 2wherein the first equation is selected from a first group consisting ofa scattering dependence equation and a time-domain signal transferdependence equation.
 4. The thermal imager of claim 2 wherein theoptical system causes the first section of the thermal images to havedifferent properties from a second section of the thermal images, andfurther wherein the processor applies a plurality of second numericalcoefficients for a second spatial filter in a second section of thethermal images, wherein the second numerical coefficients represent asecond distribution of pixel cross-talk energy in a second section ofthe thermal images and the second spatial filter is applied to enhanceobject edge contrast in the second section of the thermal images.
 5. Thethermal imager of claim 4 wherein the second numerical coefficients forthe second spatial filter are determined by: curve-fitting a secondsubset of the pixel test values in the second section of the test imageto a second equation, wherein the second equation characterizes thesecond distribution of pixel cross-talk energy in the second section ofthe test image; and using the second equation to generate the secondnumerical coefficients as a function of their representative pixelpositions within the second spatial filter.
 6. The thermal imager ofclaim 5 wherein the second equation is selected from a second groupconsisting of a scattering dependence equation and a time-domain signaltransfer dependence equation.
 7. The thermal imager of claim 1, furthercomprising a memory configured to store the first spatial filter and theimages captured by the focal plane array.
 8. The thermal imager of claim7 wherein the memory further contains gain and offset calibrationcoefficients to be applied to the thermal images.
 9. A thermal imager,comprising: means for thermal imaging; means for capturing thermalimages imaged by the means for thermal imaging, wherein the thermalimages each comprise a plurality of values for each pixel in the focalplane array; and means for applying a spatial filter to the plurality ofvalues to enhance object edge contrast in the thermal images.
 10. Amethod for correcting a thermal image produced by a calibrated thermalimager using a plurality of numerical coefficients for a spatial filtercorresponding to predetermined dimensions, comprising: capturing thethermal image with a focal plane array in the calibrated thermal imager,wherein the thermal image comprises a plurality of pixel brightnessvalues; and applying the numerical coefficients to a local matrix ofpixel brightness values in a neighborhood of each pixel of the thermalimage to obtain a corrected thermal image having enhanced object edgecontrast, wherein the numerical coefficients represent a distribution ofpixel cross-talk energy, and the local matrix has the predetermineddimensions, and further wherein the numerical coefficients are stored ina memory in the calibrated thermal imager.
 11. The method of claim 10wherein applying the numerical coefficients to a local matrix of pixelbrightness values in a neighborhood of each pixel of the image,comprises: calculating for each pixel a local difference matrix havingthe predetermined dimensions, wherein each element of the localdifference matrix is a difference between a first pixel brightness valueof that pixel and a corresponding neighboring pixel brightness value;performing for each pixel an element by element matrix multiplication ofthe local difference matrix for that pixel and the stored numericalcoefficients to obtain a parasitic energy matrix, wherein the storednumerical coefficients are in a matrix; summing elements in theparasitic energy matrix for each pixel to obtain a parasitic energycontribution for that pixel and adding the parasitic energy contributionto the pixel brightness value of that pixel; and for each neighboringpixel for each pixel, subtracting a corresponding value in the parasiticenergy matrix from the pixel brightness value for that neighboringpixel.
 12. The method of claim 10, further comprising: storing thecorrected thermal image in a memory in the calibrated thermal imager.13. The method of claim 10 wherein the numerical coefficients aredetermined by: capturing a test image of a target with the thermalimager, wherein the target has at least one skewed transition edgebetween two substantially uniformly illuminated areas, and the two areashave different illumination levels, and further wherein the test imagecomprises a plurality of pixel test values; curve-fitting a subset ofthe pixel test values in a section of the test image to an equation,wherein the equation characterizes the distribution of parasitic energyin the section of the test image; and using the equation to generate thenumerical coefficients as a function of their representative pixelpositions within the spatial filter.
 14. The method of claim 13 whereinthe equation is a scattering dependence equation.
 15. The method ofclaim 13 wherein the equation is a time-domain signal transferdependence equation.
 16. The method of claim 10, wherein applying thenumerical coefficients occurs in real-time or during post-processing.17. A thermal imager, comprising: an optical system for thermal imaging;a focal plane array for capturing thermal images imaged by the opticalsystem, wherein the thermal images each comprise a plurality of valuesfor each pixel in the focal plane array; and a memory containing a firstspatial filter for applying to the plurality of values to enhance objectedge contrast in the thermal images; and a transmitter for sending thethermal images and the first spatial filter to an external processor forcorrecting the thermal images using the first spatial filter.
 18. Thethermal imager of claim 17, wherein the first spatial filter comprises aplurality of first numerical coefficients that represent a firstdistribution of pixel cross-talk energy in a first section of thethermal images that are determined by: capturing a test image of atarget with the thermal imager, wherein the target has at least oneskewed transition edge between two substantially uniformly illuminatedareas, and the two areas have different illumination levels, and furtherwherein the test image comprises a plurality of pixel test values;curve-fitting a first subset of the pixel test values in the firstsection of the test image to a first equation, wherein the firstequation characterizes the first distribution of pixel cross-talk energyin the first section of the test image; and using the first equation togenerate the first numerical coefficients as a function of theirrepresentative pixel positions within the first spatial filter.
 19. Thethermal imager of claim 18, wherein the optical system causes the firstsection of the thermal images to have different properties from a secondsection of the thermal images, and further wherein the memory contains aplurality of second numerical coefficients for a second spatial filterin a second section of the thermal images, wherein the second numericalcoefficients represent a second distribution of pixel cross-talk energyin a second section of the thermal images and the second spatial filteris applied to enhance object edge contrast in the second section of thethermal images.
 20. The thermal imager of claim 19, wherein the secondnumerical coefficients for the second spatial filter are determined by:curve-fitting a second subset of the pixel test values in the secondsection of the test image to a second equation, wherein the secondequation characterizes the second distribution of pixel cross-talkenergy in the second section of the test image; and using the secondequation to generate the second numerical coefficients as a function oftheir representative pixel positions within the second spatial filter.21. The thermal imager of claim 17, wherein the memory further containsgain and offset calibration coefficients, and further wherein thetransmitter further sends the gain and offset calibration coefficientsto the external processor to be applied to the thermal images.